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🦠 ForamDeepSlice: AI-Powered Foraminifera Classification
Automated Fossil Species Identification from Micro-CT Scans Using Deep Learning
Revolutionizing micropaleontology with AI!
Foraminifera are microscopic organisms whose fossilized shells reveal Earth's climate history. ForamDeepSlice uses deep learning to automatically classify 12 foraminifera species from micro-CT scans with unprecedented accuracy.
🎯 Key Results:
- 95.6% accuracy across 12 species
- 99.6% top-3 accuracy
- 109,617 2D slices from 97 specimens
- Interactive dashboard for real-time classification
📊 Research Overview
Dataset
- 97 micro-CT scanned specimens
- 27 species total, 12 selected for training
- 109,617 high-quality 2D slices
- Rigorous specimen-level data splitting
Methods
- 7 state-of-the-art CNN architectures tested
- Novel PatchEnsemble strategy
- Transfer learning from ImageNet
- Advanced data augmentation
Top Performing Models
| Model | Accuracy | F1-Score |
|---|---|---|
| ForamDeepSlice (Ensemble) | 95.6% | 95.0% |
| ConvNeXt-Large | 95.1% | 94.1% |
| NASNet | 93.7% | 92.5% |
| ResNet101V2 (Baseline) | 84.3% | 80.8% |
Most species achieved F1-scores exceeding 90%, with the best reaching 99.7%. Our PatchEnsemble approach significantly improved classification of challenging species.
💻 Interactive Dashboard
<color #000000> Classification Features:
- Drag-and-drop image upload
- Automatic preprocessing & segmentation
- Real-time species prediction
- Confidence scores & rankings
- Works with micro-CT and optical images
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<color #000000> 3D Slice Matching:
- Find slice orientation in 3D models
- Advanced similarity metrics (SSIM, NCC, Dice)
- Interactive 3D visualization
- No coding required
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🌟 Impact & Applications
<color #000000> Scientific Contributions:
- First comprehensive micro-CT foram classification dataset
- Accelerates fossil identification workflows (reduces expert time)
- Supports biostratigraphy and climate reconstruction research
- Fully reproducible Docker environment
- Open-source framework for domain scientists
Future Applications:
- Expand to additional species
- Mobile applications for field use
- Integration with geological context
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Note: ForamDeepSlice is a decision-support tool for experts, complementing—not replacing—careful taxonomic analysis.
📚 Resources
Project Team: Abdelghafour Halimi, Didier Barradas-Bautista, Ronell Sicat (KVL) | Ali Alibrahim, Abdulkader M. Afifi (PSE Division)
Acknowledgments: KAUST Core Labs, Supercomputing Core Lab (Ibex), Domingo Lattanzi-Sanchez (Geo-Energy Platform)